An adaptive local binary pattern for 3D hand tracking

• We propose an adaptive local binary pattern (ALBP) for depth image based applications.
• The proposed ALBP is invariant to both rotation and depth distance in range images.
• Using ALBP, we can extract object feature without using texture or color information.
• We apply the proposed ALBP for hand tracking using depth images.
• The tracking accuracy is compared with several existing hand trackers.

Ever since the availability of real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor, the performance of gesture recognition can be largely enhanced. However, since conventional two-dimensional (2D) image based feature extraction methods such as local binary pattern (LBP) generally use texture information, they cannot be applied to depth or range image which does not contain texture information. In this paper, we propose an adaptive local binary pattern (ALBP) for effective depth images based applications. Contrasting to the conventional LBP which is only rotation invariant, the proposed ALBP is invariant to both rotation and the depth distance in range images. Using ALBP, we can extract object features without using texture or color information. We further apply the proposed ALBP for hand tracking using depth images to show its effectiveness and its usefulness. Our experimental results validate the proposal.